File size: 9,064 Bytes
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import json
import os
import uuid
import zipfile
import numpy as np
from datetime import datetime
from typing import List, Dict, Tuple, Optional
from backend.config import SQLITE_DB_PATH, OBJECT_IMAGES_DIR
from backend.database.models import TaughtObject, ObjectEmbedding, DetectionLog
class DatabaseManager:
def __init__(self, db_path: str = SQLITE_DB_PATH):
self.db_path = db_path
self.init_db()
def get_connection(self):
conn = sqlite3.connect(self.db_path)
conn.row_factory = sqlite3.Row
return conn
def init_db(self):
with self.get_connection() as conn:
cursor = conn.cursor()
cursor.execute("""
CREATE TABLE IF NOT EXISTS users (
id TEXT PRIMARY KEY,
name TEXT NOT NULL,
created_at TEXT NOT NULL
);
""")
cursor.execute("""
CREATE TABLE IF NOT EXISTS objects (
id TEXT PRIMARY KEY,
user_id TEXT,
name TEXT UNIQUE NOT NULL,
category TEXT NOT NULL,
description TEXT,
status TEXT DEFAULT 'active',
created_at TEXT NOT NULL,
updated_at TEXT NOT NULL
);
""")
cursor.execute("""
CREATE TABLE IF NOT EXISTS object_images (
id TEXT PRIMARY KEY,
object_id TEXT NOT NULL,
file_path TEXT NOT NULL,
image_hash TEXT,
created_at TEXT NOT NULL,
FOREIGN KEY (object_id) REFERENCES objects(id) ON DELETE CASCADE
);
""")
cursor.execute("""
CREATE TABLE IF NOT EXISTS object_embeddings (
id TEXT PRIMARY KEY,
object_id TEXT NOT NULL,
model_name TEXT NOT NULL,
model_version TEXT NOT NULL,
embedding TEXT NOT NULL,
created_at TEXT NOT NULL,
FOREIGN KEY (object_id) REFERENCES objects(id) ON DELETE CASCADE
);
""")
cursor.execute("""
CREATE TABLE IF NOT EXISTS detection_logs (
id TEXT PRIMARY KEY,
user_id TEXT,
object_name TEXT NOT NULL,
detection_type TEXT NOT NULL,
confidence REAL NOT NULL,
timestamp TEXT NOT NULL
);
""")
conn.commit()
def create_object(self, name: str, category: str = "general", description: str = "") -> TaughtObject:
now = datetime.utcnow().isoformat()
obj_id = f"obj_{uuid.uuid4().hex[:10]}"
with self.get_connection() as conn:
cursor = conn.cursor()
cursor.execute(
"INSERT INTO objects (id, name, category, description, created_at, updated_at) VALUES (?, ?, ?, ?, ?, ?)",
(obj_id, name.strip(), category.strip(), description.strip(), now, now)
)
conn.commit()
return TaughtObject(id=obj_id, name=name, category=category, description=description, created_at=now)
def get_object_by_name(self, name: str) -> Optional[TaughtObject]:
with self.get_connection() as conn:
cursor = conn.cursor()
cursor.execute("SELECT * FROM objects WHERE LOWER(name) = LOWER(?)", (name,))
row = cursor.fetchone()
if row:
return TaughtObject(
id=row["id"],
name=row["name"],
category=row["category"],
description=row["description"],
status=row["status"],
created_at=row["created_at"]
)
return None
def get_all_objects(self) -> List[TaughtObject]:
objects = []
with self.get_connection() as conn:
cursor = conn.cursor()
cursor.execute("""
SELECT o.*,
(SELECT COUNT(*) FROM object_images WHERE object_id = o.id) as img_cnt,
(SELECT COUNT(*) FROM object_embeddings WHERE object_id = o.id) as emb_cnt
FROM objects o
ORDER BY o.created_at DESC
""")
rows = cursor.fetchall()
for r in rows:
objects.append(TaughtObject(
id=r["id"],
name=r["name"],
category=r["category"],
description=r["description"] or "",
status=r["status"],
created_at=r["created_at"],
image_count=r["img_cnt"],
embedding_count=r["emb_cnt"]
))
return objects
def delete_object(self, object_id: str) -> bool:
with self.get_connection() as conn:
cursor = conn.cursor()
cursor.execute("DELETE FROM object_embeddings WHERE object_id = ?", (object_id,))
cursor.execute("DELETE FROM object_images WHERE object_id = ?", (object_id,))
cursor.execute("DELETE FROM objects WHERE id = ?", (object_id,))
conn.commit()
return cursor.rowcount > 0
def add_embedding(self, object_id: str, embedding: List[float], model_name: str = "resnet18", model_version: str = "1.0") -> str:
emb_id = f"emb_{uuid.uuid4().hex[:10]}"
now = datetime.utcnow().isoformat()
emb_json = json.dumps(embedding)
with self.get_connection() as conn:
cursor = conn.cursor()
cursor.execute(
"INSERT INTO object_embeddings (id, object_id, model_name, model_version, embedding, created_at) VALUES (?, ?, ?, ?, ?, ?)",
(emb_id, object_id, model_name, model_version, emb_json, now)
)
conn.commit()
return emb_id
def add_image_record(self, object_id: str, file_path: str) -> str:
img_id = f"img_{uuid.uuid4().hex[:10]}"
now = datetime.utcnow().isoformat()
with self.get_connection() as conn:
cursor = conn.cursor()
cursor.execute(
"INSERT INTO object_images (id, object_id, file_path, created_at) VALUES (?, ?, ?, ?)",
(img_id, object_id, file_path, now)
)
conn.commit()
return img_id
def get_all_embeddings(self) -> List[Tuple[str, str, np.ndarray]]:
"""Returns list of (object_id, object_name, embedding_array)"""
results = []
with self.get_connection() as conn:
cursor = conn.cursor()
cursor.execute("""
SELECT e.embedding, o.id, o.name
FROM object_embeddings e
JOIN objects o ON e.object_id = o.id
WHERE o.status = 'active'
""")
rows = cursor.fetchall()
for r in rows:
emb_vec = np.array(json.loads(r["embedding"]), dtype=np.float32)
results.append((r["id"], r["name"], emb_vec))
return results
def find_best_matching_object(self, query_embedding: np.ndarray, min_threshold: float = 0.65) -> Tuple[Optional[str], float]:
"""Performs cosine similarity search over stored object embeddings"""
all_embs = self.get_all_embeddings()
if not all_embs:
return None, 0.0
query_norm = query_embedding / (np.linalg.norm(query_embedding) + 1e-7)
best_name = None
best_similarity = -1.0
for obj_id, obj_name, emb_vec in all_embs:
emb_norm = emb_vec / (np.linalg.norm(emb_vec) + 1e-7)
sim = float(np.dot(query_norm, emb_norm))
if sim > best_similarity:
best_similarity = sim
best_name = obj_name
if best_similarity >= min_threshold:
return best_name, best_similarity
return None, best_similarity
def log_detection(self, object_name: str, detection_type: str, confidence: float):
now = datetime.utcnow().isoformat()
log_id = f"log_{uuid.uuid4().hex[:10]}"
with self.get_connection() as conn:
cursor = conn.cursor()
cursor.execute(
"INSERT INTO detection_logs (id, object_name, detection_type, confidence, timestamp) VALUES (?, ?, ?, ?, ?)",
(log_id, object_name, detection_type, confidence, now)
)
conn.commit()
def export_database_zip(self, export_path: str):
with zipfile.ZipFile(export_path, 'w') as zipf:
if os.path.exists(self.db_path):
zipf.write(self.db_path, arcname="database.db")
manifest = {
"version": "1.0",
"exported_at": datetime.utcnow().isoformat(),
"object_count": len(self.get_all_objects())
}
zipf.writestr("manifest.json", json.dumps(manifest, indent=2))
db = DatabaseManager()
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